Ultra-Grained Channel Fingerprint Construction via Conditional Generative Diffusion Models

Accurate channel state information (CSI) acquisition for massive multiple-input multiple-output (MIMO) systems is essential for future mobile communication networks. Channel fingerprint (CF) is a key enabler for intelligent environment-aware communication and can facilitate CSI acquisition. However, due to the cost limitations of practical sensing nodes and test vehicles, the resulting CF is typically coarse-grained, making it insufficient for wireless transceiver design. In this work, we propose a conditional generative diffusion model (CGDM) to bridge the relationship between coarse-grained and fine-grained CFs. Specifically, we employ a variational inference technique to derive the evidence lower bound (ELBO) for the log-marginal distribution of the observed fine-grained CF conditioned on the coarse-grained CF, enabling the CGDM to learn the complicated distribution of the target data. During the denoising neural network optimization, the coarse-grained CF is introduced as side information to guide the generation of the CGDM in a directed manner. Experimental results demonstrate that the proposed approach achieves competitive reconstruction performance and generalization capability compared to baseline methods in typical wireless communication scenarios.